A PREDICTIVE OPERATING CONTROL SYSTEM BASED ON DATA DRIVEN BAYESIAN NETWORKS

This paper reports a first step towards the implementation of a digital twin of an upper tier Seveso plant, which can predict the behavior of the system (failures, risks, malfunctions, errors) in order to operate effectively in safety. The system, based on machine learning algorithms and Bayesian reasoning, learns continuously from the data provided by the physical system. From the operational experience of the coastal storage facility, it is clear how most of the accidental events are due to a wrong arrangement of the valves, to abnormal transfer pressures, to pump failures and pipe deterioration. This paper is focused on building an operational management system, based on the operational instruction, suitable to predict operational errors and accordingly avoiding them and thus protecting asset integrity and improve aging management

Hydrogen jet-fire: Accident investigation and implementation of safety measures for the design of a downstream oil plant

As amply known, hydrogen plays a very significant role in the process industry exerting a vital functionality in oil refineries, namely for secondary level refining units such hydro-treating and hydrocracking sections. This paper starts from a statistical analysis on hydrogen accidents and a thorough investigation on the sequence and causes of an accident involving a hydrogen leakage in a downstream oil industry. We present some key features of the accident and comment some practical implications for setting up risk reduction options at the plant level. The applicative phase of the paper states the main prevention strategies and suggest possible mitigation measures for hydrogen leaks events, discussing some practical solutions applied in the design of a large refinery. The experience and lessons learned gained from the event investigation and the comparison of the accident with the predictions of the safety report leads to the formulation of proposals and design modifications aiming at preventing or at least minimizing the consequences

Ageing and creeping management in major accident plants according to seveso III directive

The focus of this paper is the management of critical equipment ageing within the context of lower and upper tier Seveso process plants, with a peculiar insight into the effectiveness of safety management systems in setting-up reliable procedure for critical element identification. Recent research studies in fact evidenced that in Europe nearly 50% of major 'loss of containment' events, arising from technical plant failures, were primarily due to ageing plant mechanisms such as erosion, corrosion and fatigue. The critical ageing elements should be included in maintenance, inspection and periodic monitoring programs in relation to their reliability, as assumed in the risk assessment and their lifetime or frequency ranges, based on their operational experience. This paper will accurately discuss how the issue of ageing is currently handled in the process industry. The methodology builds on the critical results of actual findings from the inspections on the safety management systems of major accident plants, which were performed by a working group. The primary objective is to stimulate the introduction of effective ageing management changes into the safety management of companies, by taking advantages of findings of the previous assessment and establishing proper and effective audits

An oil pipeline catastrophic failure: Accident scenario modelling and emergency response development

In spite of advanced technologies, inherent safety and safety management system, pipeline loss of containments and large-scale releases of hazardous substances are still common accidents leading to severe consequences for human health, environment and assets, both in Europe and in developing Countries. This paper presents a detailed analysis of the catastrophic failure of a pipeline connecting the port oil terminal with a downstream oil plant, in the North part of Italy, causing a major oil spill into a river and subsequently into the Genoa harbor (Italy). Firstly, the impact of atmospheric dispersion is evaluated then, assuming oil containment failure, the hydrodynamic dispersion of the spill into the sea is studied. By means of numerical methods, we performed a consequence-based assessment incorporating the effects, the hazardous distance and the reaction time scale, related to oil spill. Results are focused on the atmospheric dispersion of the "key" oil volatile fractions and the propagation in the sea of the medium-heavy fractions, both performed by Lagrangian simulations

Multicomponent dispersion of hydrocarbons at sea: Source term evaluation and hydrodynamic simulation of the spill

The evaluation of the ecological consequences of spill accidents including factors such as persistence, and long-term exposure became more stringent following some notable events and still represents an up-to-date research issue. This study focuses on the possibility of proper source identification of a multi component sea spill and is motivated by actual findings of small quantities of floating and/or stranded waxy material in some areas of Liguria and Tuscany, in early summer 2017. The paper presents a thorough hydrodynamic simulation, carried out with an oil-spill Lagrangian particle model to study the trajectories of the different released material and determine its probable source. A short-cut mathematical simulation of the step-by-step process of formation by on-board activities and subsequent water interaction is proposed so as to highlight the limitations and the potential applicability of the method, also in light of setting-up early warning systems to prevent stranding of the oil slick in sensitive coast environments

Integrated Risk Assessment of a Dangerous Goods Container Terminal. a Bow-tie Approach

Global trade continues to grow, with an increasing movement of dangerous goods in the supply chain, causing safety concerns. As a significant hub for dangerous goods transport in the Mediterranean region, Genoa Port possibly will develop new container terminals to accommodate the growing load. The proximity of the port to residential areas to the highway and the airport imposes a significant responsibility to assess operational risks and mitigate potential catastrophic events. This study focused on preliminary operational risk assessment using statistical analysis and the Bow-Tie method, which involved analysing the IMO classes to be handled in the terminal as well as accident scenarios based on the most hazardous materials associated with the IMO classes. Due to the increasing effects of climate changes, digitalization and energy transition, potentially adding further hazards during operations, a benchmark needs to be developed, also in view of future applications relying on additional smart and data-driven tools/technologies and statistically significant dataset. The findings of this study can be beneficial for the designing stage of the container terminal, regulatory authorities, stakeholders involved in the transportation and HazMat storage

Unveiling the Achilles' Heel: Detecting Organizational Weaknesses in the Energetic Transition Challenge

To be able to thrive in the grand challenges of the current historical moment, which includes important driving phenomena such as climate change, digitalization and energy transition, the organizations need a comprehensive understanding of the organizational and technical aspects that may pose opportunities and risks. This paper presents a novel approach to identify weak organizational and technical factors within the context of the energetic transition challenge. To accomplish this, a Machine Learning system is proposed, that integrates, as input features, escalation and mitigation factors related to the risks that may arise in relation to the energetic transition. The target variable is an indicator concerning the possible increase in the probability of accidents and near misses, which is selected as an effective detector of potential weaknesses in the system. The primary objective is to uncover organizational aspects that influence the mitigation, or enhancement of technological risks during the energetic transition. By analysing the interplay between organizational and technical factors and their role on preventive and mitigating barriers, this paper aims at identifying critical areas that require Attention and improvement to ensure a smooth and successful energetic transition process. A reference case-study is presented to demonstrate the actual capability of the presented framework. The findings of this study have practical implications in the definition of organizational priorities in managing the energetic transition; the identified weaknesses can serve as a basis for targeted interventions and strategic decision-making, allowing for more effective risk management and improved outcomes during the energy transition

Understanding the Vulnerability of Complex Systems. An Integrated Approach

The increasing complexity of current system realities (e.g., pandemics, healthcare, energy transition, process industry 4.0 etc.) would require the evaluation of the actual way systems are modified, often referred with “work as done”, rather than “work as imagined”. The safety of a complex system is one of the emergent properties depending upon the interactions between the system's components and subsystems. This paper is focused on the analysis of the nature of the interactions within a complex system when it is subjected to cumulative stresses, crises and accidents. The objective is to identify, test and validate integrated emergency management procedures in the event of accidents, crises or major incidents occurring during the loading and unloading of goods and hazardous substances. To test the applicability of the framework, we developed a prototypal application identifying as a target complex system an Italian port area. An interactive simulation model was ad-hoc designed and developed, which makes it possible to reproduce the evolution of the crisis and its impact on structures, systems, people and goods by considering both the physical aspects and the domino effect in a multiple/sequential accidental scenario simulation. Additionally, it is possible testing the effectiveness of new technological and infrastructural solutions to reduce vulnerability, mitigate damage and prevent possible escalation of the event. Relevant accident scenarios were firstly thoroughly selected and subsequently integrated into a digital twin of the port. The interactivity allows a dynamic simulation of the possible actions of the different elements and active subsystems considered as a complex system, exploring their interactions in the face of crises and disasters, including the determining role of human factor

A Dynamic Approach to Natech Risk Assessment Applied to an LPG Storage Facility in a Landslides Sensitive Italian Area

Due to the climate change, extreme weather phenomena are becoming increasingly intense and occur with higher frequencies, even in unusual areas. Nevertheless, historical data showed as Natech accidents can be triggered not only by natural disasters, like earthquakes or tornadoes, but even by natural phenomena that are considered of minor importance, such as rain and lightning. Only recently, the Natech issue has gained a great deal of attention, but there is still a lack of consolidated Natech risk-analysis methodologies and tools. The focus of this work is to include natural hazards into a dynamic risk assessment system beside the typical parameters of process risks. In Italy, rainfall represents the most common triggering factor for landslides. Generally, the determination of trigger and propagation can rely on physically-based approaches, which require the calibration of many parameters and are often difficult to apply, or on empirical correlations between rainfall and landslide built from historical data. On the other hand, by using a data driven approach, available data can be exploited to define the system state over time, anticipate the systems outcome, support decision-making, and adopt the most appropriate adjustments, allowing to enhance system resilience and knowledge. The actual capability of the proposed approach was evaluated on a simple case-study represented by an LPG storage facility located in landslides sensitive zone of Liguria Region

DARMS - Dynamic Asset-integrity and Risk Management System - How Machine Learning and Systems Engineering cooperate to enhance the resilience of complex systems

\u201cStatic, incomplete, superficial, wrong\u201d. The traditional approach to risk analysis, as applied in the process industries, has been largely criticized in response to recent major accidents. Since it was first proposed, modifications and improvements have been made, and a formal accepted approach is included in several regulations and standards (as the recent development of guidelines for the ageing management in SEVESO installations). Quantitative Risk Assessment (QRA) is based on consolidated procedures. Nevertheless, the need of safety improvement asks for more advanced tools for hazard identification and risk evaluation. Besides considering technical aspects (e.g., malfunctions and process upsets), operational errors, organizational aspects, such as lack of attention and motivation to the safety culture, may lead to risk increment in terms of likelihood of undesired failures. Not all those aspects may be investigated with conventional QRA techniques, which have also the disadvantage of being intrinsically static and failing to capture risk variations during the lifecycle of a plant or production site. Despite their proved effectiveness, many hazards identification and risk assessment techniques lack the dynamic dimension, which is the ability to learn from new risk notions, experience, and early warnings. Now\u2019s the time to go beyond the limits of conventional static methods for hazard identification and risk assessment; the risk assessment is, indeed, a very useful approach in support of this change but at the same time it is not exhaustive to capture also the possible \u201cfailure\u201d in the interface/interaction among the several single components of a complex system beside their specific failures. This research work discusses a novel approach for dynamizing the risk assessment process, integrating measured process data, asset integrity and operative conditions. In the first part of the thesis, the inferential process and the application of Machine Learning to inference is discussed, and various applications of standard, and tailored, machine learning algorithms to industrial and environmental risks are detailed as case studies. The second part is focused on the resilience engineering. The resilience paradigm is discussed, as well as the concept of emerging properties of complex systems. it will be shown how real-time data analytics, through appropriate AI models, combined with the expert knowledge of process engineering, constitute the fundamental technological key to pursue the resilience of plants and processes. The third section integrates the aforementioned concepts within the wide framework of Systems Engineering. Accordingly, a dynamic and systemic model is presented, to address the significant shortcomings of the current risk analysis models. The Dynamic Asset-integrity and Risk Management System (DARMS) is designed starting from the Bow-tie technique, integrated with improved Machine Learning algorithms, to overcome the epistemic uncertainty in the prior probabilities and likelihoods of escalation factors and barriers. Subsequently, a Hidden Markov Model (HMM), based on Bayesian Inference, is developed to analyze real-time risk, and produce reliable predictions on the state of the whole system during the operations. The application of the proposed model is demonstrated on an Oil and Gas terminal under Seveso legislation. The results of the case study provide a better understanding of the advanced Data Driven modeling of accident scenarios. The proposed model will serve as a useful tool for the operational safety management of complex systems